The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like the Americas and Asia.

In 2022, smartphone vendors sold around 1.39 billion smartphones were sold worldwide, with this number forecast to drop to 1.34 billion in 2023.

Smartphone penetration rate still on the rise

Less than half of the world’s total population owned a smart device in 2016, but the smartphone penetration rate has continued climbing, reaching 78.05 percent in 2020. By 2025, it is forecast that almost 87 percent of all mobile users in the United States will own a smartphone, an increase from the 27 percent of mobile users in 2010.

Smartphone end user sales

In the United States alone, sales of smartphones were projected to be worth around 73 billion U.S. dollars in 2021, an increase from 18 billion dollars in 2010. Global sales of smartphones are expected to increase from 2020 to 2021 in every major region, as the market starts to recover from the initial impact of the coronavirus (COVID-19) pandemic.

The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Mexico and Canada.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Australia & Oceania and Asia.

China is leading the ranking by number of smartphone users, recording ****** million users. Following closely behind is India with ****** million users, while Seychelles is trailing the ranking with **** million users, resulting in a difference of ****** million users to the ranking leader, China. Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The data set provides estimates of country-level daily mobility metrics (uncertainty included) for 17 countries from March 11, 2020 to present. Estimates are based on more than 3.8 million smartphone trajectories. Metrics: Estimated daily average travelled distance by people. Estimated percentage of people who did not move during the 24 hours of the day. Countries: Argentina (ARG), Chile (CHL), Colombia (COL), Costa Rica (CRI), Ecuador (ECU), Greece (GRC), Guatemala (GTM), Italy (ITA), Mexico (MEX), Nicaragua (NIC), Panama (PAN), Peru (PER), Philippines (PHL), Slovenia (SVN), Turkey (TUR), United States (USA) and Venezuela (VEN). Covered period: from March 11, 2020 to present. Temporal resolution: daily. Temporal smoothing: No smoothing. 7-day moving average. 14-day moving average. 21-day moving average. 28-day moving average. Uncertainty: 95% bootstrap confidence interval. Data ownership Anonymized data on smartphone trajectories are collected, owned and managed by Futura Innovation SRL. Smartphone trajectories are stored and analyzed on servers owned by Futura Innovation SRL and not shared with third parties, including the author of this repository and his organization (University of Bergamo). Contribution Ilaria Cremonesi of Futura Innovation SRL is the data owner and data manager. Francesco Finazzi of University of Bergamo developed the statistical methodology for the data analysis and the algorithms implemented on Futura Innovation SRL servers. {"references": ["Finazzi, F. (2022) Replacing discontinued Big Tech mobility reports: a penetration-based analysis, arXiv:2210.09714"]}

Aedes aegyptiWingbeat frequency data for Aedes aegypti from various mobile phonesAedes albopictusWingbeat frequency data for Aedes albopictus from various mobile phonesAedes mediovittatusWingbeat frequency data for Aedes mediovittatus from various mobile phonesAedes sierrensisWingbeat data for Aedes sierrensis mosquitoes from the field - both raw data with noises and cleaned data with manually isolated mosquito sounds includedAnopheles albimanusWingbeat frequency data for Anopheles albimanus from various mobile phonesAnopheles arabiensisWingbeat frequency data for Anopheles arabiensis from various mobile phonesAnopheles atroparvusWingbeat frequency data for Anopheles atroparvus from various mobile phonesAnopheles dirusWingbeat frequency data for Anopheles dirus from various mobile phonesAnopheles farautiWingbeat frequency data for Anopheles farauti from various mobile phonesAnopheles freeborniWingbeat frequency data for Anopheles freeborni from various mobile phonesAnopheles gambiaeWing...

The global smartphone penetration in was forecast to continuously increase between 2024 and 2029 by in total 20.3 percentage points. After the fifteenth consecutive increasing year, the penetration is estimated to reach 74.98 percent and therefore a new peak in 2029. Notably, the smartphone penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the smartphone penetration in countries like North America and the Americas.

Digitally semi-literate means those people who face challenges in digital enablement and are not too familiar with using smartphones for text message communication. Any progress to reduce the difficulty of their smartphone usage can help these people. These people are over one billion worldwide. The dataset contains text messages in English (some of these are translations of local text messages) from semi-literate Indian users. The dataset has been derived from face to face surveys primarily. Only 10% by online surveys since these people are not comfortable in doing online surveys.

The Mozambique Population-Based Survey (PBS) provides a comprehensive assessment of the current status of agriculture and food security in two provinces, Zambizia and Nampula. These areas were selected based on national estimates that indicate that the incidence of poverty, malnutrition, and stunting among children less than five years of age is disproportionately high. These provinces are adjacent to three of the country's main trade corridors: Nacala (linking Mozambique to Malawi and Zambia), Beira (linking Mozambique to Zimbabwe), and the N1 (key North-South road connecting Nacala and Beira corridors). This spreadsheet describes the use of mobile phones and mobile banking.

App Switch Networks DatasetGML files representing the Android smartphone application switching networks of 53 individuals.networkdata.zip

This competition involves advertisement data provided by BuzzCity Pte. Ltd. BuzzCity is a global mobile advertising network that has millions of consumers around the world on mobile phones and devices. In Q1 2012, over 45 billion ad banners were delivered across the BuzzCity network consisting of more than 10,000 publisher sites which reach an average of over 300 million unique users per month. The number of smartphones active on the network has also grown significantly. Smartphones now account for more than 32% phones that are served advertisements across the BuzzCity network. The "raw" data used in this competition has two types: publisher database and click database, both provided in CSV format. The publisher database records the publisher's (aka partner's) profile and comprises several fields:

publisherid - Unique identifier of a publisher. Bankaccount - Bank account associated with a publisher (may be empty) address - Mailing address of a publisher (obfuscated; may be empty) status - Label of a publisher, which can be the following: "OK" - Publishers whom BuzzCity deems as having healthy traffic (or those who slipped their detection mechanisms) "Observation" - Publishers who may have just started their traffic or their traffic statistics deviates from system wide average. BuzzCity does not have any conclusive stand with these publishers yet "Fraud" - Publishers who are deemed as fraudulent with clear proof. Buzzcity suspends their accounts and their earnings will not be paid

On the other hand, the click database records the click traffics and has several fields:

id - Unique identifier of a particular click numericip - Public IP address of a clicker/visitor deviceua - Phone model used by a clicker/visitor publisherid - Unique identifier of a publisher adscampaignid - Unique identifier of a given advertisement campaign usercountry - Country from which the surfer is clicktime - Timestamp of a given click (in YYYY-MM-DD format) publisherchannel - Publisher's channel type, which can be the following: ad - Adult sites co - Community es - Entertainment and lifestyle gd - Glamour and dating in - Information mc - Mobile content pp - Premium portal se - Search, portal, services referredurl - URL where the ad banners were clicked (obfuscated; may be empty). More details about the HTTP Referer protocol can be found in this article. Related Publication: R. J. Oentaryo, E.-P. Lim, M. Finegold, D. Lo, F.-D. Zhu, C. Phua, E.-Y. Cheu, G.-E. Yap, K. Sim, M. N. Nguyen, K. Perera, B. Neupane, M. Faisal, Z.-Y. Aung, W. L. Woon, W. Chen, D. Patel, and D. Berrar. (2014). Detecting click fraud in online advertising: A data mining approach, Journal of Machine Learning Research, 15, 99-140.

There are a total of 17 questions in the survey, addressing the following categories:Internet useMobile phone use (smartphones & basic voice/SMS phones)Awareness and use of WikipediaGeneral demographicsThe survey collected 2500 total responses, representing populations in 5 geographical regions served by 3 mobile Iraqi operators. 3 language choices (Arabic, English, Kurdish) were provided.Here are the main questions this survey was designed to answer. However, analyzing the full data set allows you to conduct more in-depth data explorations and gain meaningful insights beyond the points presented here.What is the actual number of people who use the internet?(Real-world behavior makes this difficult to measure from industry reports, since people might have access to the internet through school, friends, internet cafés, public Wifi, etc.)For internet users: What do people mostly use the internet for?For non-internet users: Why not use the internet?How many people use smartphones?Do people with smartphones use the internet from just Wifi? Or just cellular service?How many people think that they don’t use the internet, but still use Facebook or WhatsApp?How many people have heard of Wikipedia? What do they use it for? How often?If they have heard of Wikipedia, but aren’t using it, why not?Compared to previous phone surveys in other countries, the 2017 Iraq phone survey presented new questions.What are people’s awareness of other major internet brands in comparison to Wikipedia?Can people find online content in their preferred language?How does data cost impact internet use?

Overview

The goal of this project was to create a structured dataset which can be used to train computer vision models to detect electronic waste devices, i.e., e-waste or Waste Electrical and Electronic Equipment (WEEE). Due to the often-subjective differences between e-waste and functioning electronic devices, a model trained on this dataset could also be used to detect electronic devices in general. However, it must be noted that for the purposes of e-waste recognition, this dataset does not differentiate between different brands or models of the same type of electronic devices, e.g. smartphones, and it also includes images of damaged equipment.

The structure of this dataset is based on the UNU-KEYS classification Wang et al., 2012, Forti et al., 2018. Each class in this dataset has a tag containing its corresponding UNU-KEY. This dataset structure has the following benefits: 1. It allows the user to easily classify e-waste devices regardless of which e-waste definition their country or organization uses, thanks to the correlation between the UNU-KEYS and other classifications such as the HS-codes or the EU-6 categories, defined in the WEEE directive; 2. It helps dataset contributors focus on adding e-waste devices with higher priority compared to arbitrarily chosen devices. This is because electronic devices in the same UNU-KEY category have similar function, average weight and life-time distribution as well as comparable material composition, both in terms of hazardous substances and valuable materials, and related end-of-life attributes Forti et al., 2018. 3. It gives dataset contributors a clear goal of which electronic devices still need to be added and a clear understanding of their progress in the seemingly endless task of creating an e-waste dataset.

This dataset contains annotated images of e-waste from every UNU-KEY category. According to Forti et al., 2018, there are a total of 54 UNU-KEY e-waste categories.

Description of Classes

At the time of writing, 22. Apr. 2024, the dataset has 19613 annotated images and 77 classes. The dataset has mixed bounding-box and polygon annotations. Each class of the dataset represents one type of electronic device. Different models of the same type of device belong to the same class. For example, different brands of smartphones are labelled as "Smartphone", regardless of their make or model. Many classes can belong to the same UNU-KEY category and therefore have the same tag. For example, the classes "Smartphone" and "Bar-Phone" both belong to the UNU-KEY category "0306 - Mobile Phones". The images in the dataset are anonymized, meaning that no people were annotated and images containing visible faces were removed.

The dataset was almost entirely built by cloning annotated images from the following open-source Roboflow datasets: [1]-[91]. Some of the images in the dataset were acquired from the Wikimedia Commons website. Those images were chosen to have an unrestrictive license, i.e., they belong to the public domain. They were manually annotated and added to the dataset.

Cite This Project

This work was done as part of the PhD of Dimitar Iliev, student at the Faculty of German Engineering and Industrial Management at the Technical University of Sofia, Bulgaria and in collaboration with the Faculty of Computer Science at Otto-von-Guericke-University Magdeburg, Germany.

If you use this dataset in a research paper, please cite it using the following BibTeX: @article{iliev2024EwasteDataset, author = "Iliev, Dimitar and Marinov, Marin and Ortmeier, Frank", title = "A proposal for a new e-waste image dataset based on the unu-keys classification", journal = "XXIII-rd International Symposium on Electrical Apparatus and Technologies SIELA 2024", year = 2024, volume = "23", number = "to appear", pages = {to appear} note = {under submission} }

Contribution Guidelines

Image Collection

1. Choose a specific electronic device type to add to the dataset and find its corresponding UNU-KEY. * The chosen type of device should have a characteristic design which an object detection model can learn. For example, CRT monitors look distinctly different than flat panel monitors and should therefore belong to a different class, regardless that they are both monitors. In contrast, LED monitors and LCD monitors look very similar and are therefore both labelled as Flat-Panel-Monitor in this dataset.
2. Collect images of this type of device. * Take note of the license of those images and their author/s to avoid copyright infringement. * Do not collect images with visible faces to protect personal data and comply w

The study explores the direct and mediated impacts of customers’ perception of purchase budget (BGT) on purchase intention (PIT) through perceived quality (PPQ), perceived price (PPR), and perceived benefit (PB) in a cross-country setting to understand BGT’s role in predicting customer purchase intention in smartphone selling through international online shopping platforms. An online survey was conducted in Kenya, France, and the United States to gather data from 429 consumers who had recently purchased one or more smartphones through international online shopping platforms. SmartPLS-4 was used to test the hypotheses. Results for the entire sample showed a significantly positive mediating role of PPR and PPQ between BGT and PIT. However, the mediating roles of PPQ and PB were not significant in the samples from Kenya, France, and the United States. The results also showed that PPR plays a significant and positive mediating role between BGT and PIT in samples from Kenya, France, the United States, and overall. However, the direct relationships between BGT and PPQ, PPR, and PB are shown to be negatively significant.

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

BanglaSER is a specialized dataset designed for the task of Bangla speech emotion recognition. This dataset includes a rich collection of speech-audio recordings that capture a variety of fundamental human emotions. It is curated to support research and development in the field of speech emotion recognition, particularly for the Bangla language, and is suitable for various deep learning architectures.

Dataset Composition:

• Total Number of Recordings: 1,467
• Number of Speakers: 34 (17 male and 17 female)
• Age Range of Speakers: 19 to 47 years
• Recording Devices: Smartphones and laptops

Emotional States Covered:

• Angry
• Happy
• Neutral
• Sad
• Surprise

Recording Structure:

Each emotional state is represented by:

• 3 Statements spoken three times by each participant.
• For Angry, Happy, Sad, and Surprise: 3 statements × 3 repetitions × 34 speakers = 1,224 recordings.
• For Neutral: 3 statements × 3 repetitions × 27 speakers = 243 recordings

Key Features:

Balanced Representation:

• The dataset is carefully balanced with an equal number of male and female participants, ensuring that the recordings reflect diverse voices and emotional expressions.
• Emotions are evenly distributed across the dataset, providing a robust basis for training and evaluating emotion recognition models.

Realistic Recording Conditions:

• Recordings are made using commonly available devices, such as smartphones and laptops, which helps in preserving the naturalistic quality of the audio.
• The dataset reflects real-life acoustic environments, making it more applicable to real-world applications.

Deep Learning Compatibility:

• BanglaSER is designed to be compatible with various deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and Bidirectional LSTMs (BiLSTMs).
• The dataset can be used for a range of tasks, from emotion classification to sentiment analysis, and more.

Usage and Applications:

• Emotion Recognition Models: BanglaSER provides a diverse set of recordings that are ideal for training models to recognize and classify emotions in Bangla speech.
• Benchmarking and Evaluation: The dataset serves as a benchmark for evaluating the performance of emotion recognition systems and can help in comparing different model architectures and techniques.
• Research and Development: Researchers can use BanglaSER to explore new methods in speech emotion recognition, develop novel algorithms, and enhance the understanding of emotion in speech.

Dataset Access:

Download Link: https://data.mendeley.com/datasets/t9h6p943xy/5

• Documentation: Detailed documentation and guidelines for using the dataset are provided to assist users in effectively leveraging the data.

Acknowledgments:

We extend our gratitude to the contributors and participants who made this dataset possible. Their efforts have greatly enriched the field of speech emotion recognition and provided valuable resources for the community.

Feel free to explore the dataset and utilize it in your research and projects. We look forward to seeing the innovative applications and advancements that will emerge from the use of BanglaSER

In the fourth quarter of 2024, Samsung shipped around 52 million smartphones, a decrease from the both the previous quarter and the same quarter of the previous year. Samsung’s sales consistently place the smartphone giant among the top three smartphone vendors in the world, alongside Xiaomi and Apple. Samsung smartphone sales – how many phones does Samsung sell? Global smartphone sales reached over 1.2 billion units during 2024. While the global smartphone market is led by Samsung and Apple, Xiaomi has gained ground following the decline of Huawei. Together, these three companies hold more than 50 percent of the global smartphone market share.

Switzerland is leading the ranking by population share with mobile internet access , recording 95.06 percent. Following closely behind is Ukraine with 95.06 percent, while Moldova is trailing the ranking with 46.83 percent, resulting in a difference of 48.23 percentage points to the ranking leader, Switzerland. The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The number of smartphone users in Asia was forecast to continuously increase between 2024 and 2029 by in total 1.2 billion users (+49.46 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 3.7 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Europe and Worldwide.